By reviewing the works of biovolume measurement, edge detection approaches are also widely applied [1]}, [2]}, [3]}.
The core algorithm of edge detection is to determine the edge pixels in the image first rather than connect these pixels to form the region boundary.
Sobel operator is a discrete differentiation operator, which can be applied to calculate the approximate gradient of the grayscale image [1]}.
The pixel with a large gradient is more likely to be the edge.
The Sobel operator can calculate the derivative in the horizontal and vertical directions, but it is relatively sensitive to the noise.
Hence, the pre-processing of denoising is necessary.
The canny operator is a multi-level detection algorithm that contains denoising, gradient calculation, non-maximal suppression, and the method of double thresholding [5]}.
Non-maximum suppression is an edge thinning method to retain the local maximum gradients and suppress the other gradients.
The canny operator performs well for most of the images.
In the Marr-Hildreth operator, a Gaussian filter is applied first for smoothing, and then the Laplacian is applied for image enhancement [2]}.
Finally, the point of intersection between zero axis and the second derivative line can be determined as the edge.
The comparative results of Sobel, Canny and Marr-Hildreth are shown in Fig. REF .
The segmentation results of Sobel operator have more noise obviously, and the Marr-Hildreth results have the problem of over-detection, which means many inner pixels are classified as edge.
Canny operator performs best by comparing with the other approaches.
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